Obtaining measurements of improved accuracy of one or more polymer properties with an on-line NMR system

ABSTRACT

A nuclear magnetic resonance (NMR) system, and related method, develops two or more regression equations or models for a particular polymer property of interest (e.g., melt index or MI) during a calibration procedure using known samples of a material. The polymer material can be, for example, a plastic (e.g., polyethylene, polypropylene, or polystyrene) or a rubber (e.g., ethylene propylene rubber). Regression models for one or more discrete (i.e., two-valued) variables also are developed during calibration, and these models allow a prediction to be made about which of the two or more property (e.g., MI) models should be used for any particular sample of unknown material. The prediction obtained from the discrete variable model indicates which of the two or more models will produce the most accurate estimation of the property of interest for the unknown sample. The best model is thus selected, and then it is used to estimate the property of interest.

CROSS-REFERENCE TO RELATED PATENTS AND APPLICATIONS

This application is a continuation-in-part of the following two pendingU.S. patent applications: (1) U.S. patent application Ser. No.08/370,862 filed on Jan. 10, 1995 which is a continuation-in-part ofU.S. patent application Ser. No. 08/226,061 filed on Apr. 11, 1994 (nowabandoned) which is a continuation of U.S. patent application Ser. No.794,931 filed on Nov. 20, 1991, now U.S. Pat. No. 5,302,896 issued toDechene et al. on Apr. 12, 1994; and (2) U.S. patent application Ser.No. 08/371,091 filed on Jan. 10, 1995 which is a continuation-in-part ofU.S. patent application Ser. No. 08/226,024 filed on Apr. 11, 1994 (nowabandoned) which is a continuation of U.S. patent application Ser. No.885,633 filed on May 19, 1992, now U.S. Pat. No. 5,302,897 issued toDechene et al. on Apr. 12, 1994.

Also, this application is related to the following U.S. patents: U.S.Pat. No. 5,015,954 issued to Dechene et al. on May 14, 1991; U.S. Pat.No. 5,049,819 issued to Dechene et al. on Sep. 17, 1991; U.S. Pat. No.5,319,308 issued to Dechene et al. on Jun. 7, 1994; U.S. Pat. No.5,367,260 issued to Dechene et al. on Nov. 22, 1994; and U.S. Pat. No.5,408,181 issued to Dechene et al. on Apr. 18, 1995.

Each of these U.S. patents and patent applications is of commonassignment with this application, and the disclosures of all of theabove-listed patents and patent applications are incorporated herein byreference.

FIELD OF THE INVENTION

The present invention involves using pulsed nuclear magnetic resonance(NMR) techniques to measure physical properties (e.g., xylene solubles,density, rubber/oil content, and flow rates such as melt index, flowrate ratio, melt flow) of polymer materials (e.g., rubber and plasticssuch as polypropylene, polyethylene, and polystyrene).

BACKGROUND OF THE INVENTION

The pulsed NMR techniques described herein, and in the above-identifiedrelated patents and applications, involve the use of a pulsed burst orpulse of energy designed to excite the nuclei of a particular nuclearspecies of a sample being measured. The protons, or the like, of thesample are first precessed in an essentially static magnetic field. Theprecession thus is modified by the pulse. After the application of thepulse, there occurs a free induction decay (FID) of the magnetizationassociated with the excited nuclei. That is, the transversemagnetization associated with the excited nuclei relaxes back to itsequilibrium value of zero, and this relaxation produces a changingmagnetic field which is measured in adjacent pickup coils. Arepresentation of this relaxation is the FID waveform or curve.

An NMR system described herein, and in the above-identified relatedpatents and applications, takes measurements of physical properties ofpolymer materials (e.g., rubber, plastics, etc.) and relates thoseproperties back to, for example, flow rates (e.g., melt index),crystallinity, composition, density, and tacticity by performing thefollowing analysis methods. The NMR system is first calibrated withknown samples by determining the physical types, properties, andquantities of the target nuclei in each known sample. Unknown samplesare then introduced into the NMR system, and the system determines thephysical types, properties, and quantities of the target nuclei in eachunknown sample based on the calibration information.

The analysis methods performed by the NMR system involve decomposing anFID curve associated with a known sample into a sum of separate timefunction equations. Useful time function equations include Gaussians,exponentials, Abragams (defined herein as Gaussian multiplied by thequantity sin(ωt) divided by ωt), modified exponential (defined herein asCe^(-z) where C is a constant, z=(kt).sup.α, and α lies between 0 and 1or 1 and 2), modified Gaussian (defined herein as Gaussian multiplied bythe cosine of the square root of t), and trigonometric.

The coefficients of the time function equations are derived from the FIDby use of a Marquardt-Levenberg (M-L) iterative approximation thatminimizes the Chi-Squared function. This iterative technique iswell-known in the art. Other known-in-the-art iterative techniques canbe used instead of M-L including a Gauss-Jordan technique, aNewton-Raphson technique, and/or a "steepest descent" technique.

From the time functions, a set of parameters is calculated. Some ofthese parameters are ratios of the y-axis intercepts, decay times (ortime constants of decay) for each of the time functions, and the crossproducts and reciprocals thereof. The sample temperature may form thebasis for another parameter.

Statistical modeling techniques are then used to select a subset ofthese parameters to form a regression equation or model. Regressioncoefficients are then computed for this parameter subset. Theseregression coefficients represent the regression model which relates theparameter subset to the types, properties, and quantities of the targetnuclei in the known sample.

After the NMR system has been calibrated with one or more known samples,unknown samples can be introduced thereinto.

When an unknown sample is introduced into the calibrated NMR system, theFID associated with the unknown sample is decomposed into a sum ofseparate time function equations. The coefficients of the time functionequations are derived from the FID by use of the iterative M-Ltechnique. From the time functions, parameters are calculated. Theparameters are then "regressed" via the regression model to yield thetypes, properties, and quantities of the target nuclei in the unknownsample. That is, the measured parameters from the FID of the unknownsample are used with the regression model, and the types, properties,and quantities of the target nuclei in the unknown sample aredetermined. This information is related to a property of thesample-under-test such as density, xylene solubles, or flow rates (e.g.,melt index, flow rate ratio, and/or melt flow).

It should be understood that the regression model is a multi-dimensionalregression equation or model, and it may be linear or non-linear.Because it is multi-dimensional, it may not be graphically represented.As an example of a regression technique, consider that the grade pointaverage of each of the students at a college were related to thatstudent's SAT score and high school standing, forming a threedimensional space. The line formed is a "regression line" which may begraphed. A new student's grade point average may be predicted by placingthe new student's SAT score and high school standing on the "regressionline" and "reading" the grade point average.

Melt index (MI) has been defined for polyethylene as the flow rateobtained under condition 190/2.16 (note 19, ASTM, or American Societyfor Testing and Materials, No. D1238-90b). In general, MI is a measureof viscosity. Flow rate ratio (FRR) has been defined for polyethylene asa dimensionless number derived by dividing the flow rate at condition190/10 by the flow rate at condition 190/2.16 (paragraph 8.3, page 396,ASTM No. D1238-90b). In some cases, the logarithm of FRR is used insteadof FRR. Melt flow (MF) has been defined for polypropylene as the flowrate at condition 230/2.16 (ASTM No. D1238-90b).

For some polymer materials, the NMR system may produce unacceptableresults. For example, for some polyethylene "grades" (i.e., polyethyleneproducts having specified density and MI values), the standard deviationbetween the MI estimation obtained by the NMR system and the "actual" MIvalue (e.g., the MI value obtained by a manual laboratory ASTM method)can become unacceptable if the calibration is carried out over theentire product "slate" (i.e., the totality of product gradesmanufactured by a particular producer).

SUMMARY OF THE INVENTION

It has been found that the relationship of some polymer properties(e.g., MI or average molecular weight) with NMR parameters (ortransforms) tends to be strongly non-linear over a broad product slate.It is desirable to divide a broad slate into smaller segments over whichbetter linearity holds. Polymer property (e.g., MI) regressions are thusgreatly facilitated. This has been found to be the case with, forexample, smaller MI's (or higher molecular weights). Similarly, somepolymer materials such as polyethylene with, for example, higher density(e.g., greater than 0.95) tend to be very rigid, whereas lower density(e.g., less than 0.92) grades are more mobile. In fact, it has beenfound that for acceptable MI calibration, polyethylene with a densityless than or equal to about 0.92 may need to be treated separately frompolyethylene of higher densities.

A regression model prepared from samples only in a narrow product graderange has been found to yield acceptable and improved results for somepolymer property calibrations such as a melt index (MI) calibration.

It is an object of this invention to improve the accuracy of theestimations of polymer properties (e.g., MI) produced by an on-linenuclear magnetic resonance (NMR) system for various polymer materials.In one particular embodiment, the NMR system produces MI measurementshaving standard deviations less than 10%, and more preferably about 5-7%or less.

It is also an object of this invention to use a regression model todetermine more accurately a property of interest (e.g., xylene solubles,density, rubber/oil content, or flow rates such as MI, FRR, and MF) of aplastic (e.g., polypropylene, polyethylene, or polystyrene) or any otherpolymer including rubber. The techniques according to the invention arefor use "on-line" and do not require any external input, other than theunknown sample itself, in order to determine accurately the sample'sproperty of interest. The appropriate regression model is selectedautomatically, without any operator or user input or interaction, from aplurality of different regression models associated with the particularproperty of interest.

The NMR system and method according to the invention develops two ormore regression equations or models for a polymer property such as MI(e.g., MI₁, MI₂, and MI₃) during a calibration procedure using knownsamples. Regression models for one or more discrete (i.e., two-valued)variables also are developed during calibration, and these models allowa prediction to be made about which of the plurality of MI regressionmodels should be used for any particular unknown sample. If there aretwo regression models for the particular polymer property of interest,only a single discrete variable regression model is needed, and the twopossible values of the variable correspond to the two property models.That is, in the case of two property models, the single discretevariable model yields a value (one of the two possible values) whichcorresponds to the property model that will produce the most accuratemeasurement of that property for an unknown sample. If there are Nregression models for the property of interest and N is greater thantwo, N discrete variable regression models are used and each onecorresponds to a different one of the N property models. In this case,the property model that corresponds to the discrete variable regressionmodel yielding the largest value is selected as the property model thatwill produce the most accurate measurement of the property of interestfor the unknown sample. In either case, after having selected one of theplurality of property regression models by using the discrete variableregression model(s), the selected property model is applied to dataderived from the unknown sample to determine the property of interest(e.g., MI) of the unknown sample.

The discrete variable is created prior to model making based on, forexample, laboratory density and MI data for polyethylene samples orlaboratory solubles/tacticity and MF data for polypropylene. This datais gathered prior to model making from various sources such as users ofNMR systems conforming to the descriptions in the above-listed relatedpatents and applications. For example, the discrete variable can be aBoolean variable which has a certain value (e.g., 1) for polyethylenewith a density ≦0.9225 grams/milliliter and an MI≦1.25 and another value(e.g., 0) for any other density/MI grade polyethylene. It should beunderstood that more complex conditions might be applied such as densityand/or MI within a certain range (e.g., 0.91<density<0.9225 and0.2<MI<1.25). Using such a discrete variable or variables results in MImeasurements being acceptable regardless whether an unknown sample isoutside or inside of the density/MI range identified above. In oneembodiment, an acceptable MI measurement has a standard deviation lessthan 10%, and preferably about 5-7% or less. If the sample ispolyethylene falling within the range, a certain MI regression model isused. If the sample falls outside of the range, a different MIregression model is used. Regardless of the particular grade of thesample, the invention automatically selects the MI or other polymerproperty model which results in the most accurate measurement for thatproperty.

In accordance with the invention, a plurality of regression models for avariety of polymer properties (e.g., MI, xylene solubles, density,rubber/oil content, FRR, or MF) can be developed during the calibrationprocedure. For example, two or more regression models for rubber content(e.g., R₁ and R₂) can be developed. In this case, the discrete Booleanvariable regression model allows a prediction to be made about which ofthe two rubber content regression models should be used for anyparticular unknown sample. After the rubber content model is selected byusing the discrete variable model, the selected rubber contentregression model is applied to data derived from the unknown sample todetermine the rubber content of the unknown sample.

The foregoing and other objects, aspects, features, and advantages ofthe invention will become more apparent from the following descriptionand from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the sameparts throughout the different views. Also, the drawings are notnecessarily to scale, emphasis instead generally being placed uponillustrating the principles of the invention.

FIGS. 1A, 1B and 1C are block/schematic diagrams of a preferredembodiment of a pulsed NMR system according to the invention which issuitable for measuring various properties of a range of polymermaterials.

FIGS. 2A-2D are diagrams of other embodiments for handling samples.

FIG. 3 is a diagram showing a sensor for measuring the sampletemperature.

FIG. 4 is a graphical representation of a free induction decay (FID)curve and its component curves measured for polyethylene.

FIG. 5A is a flowchart of steps performed according to the invention todetermine properties of a polymer sample.

FIGS. 5B and 5C are graphs of NMR measurements of density versus NMRmeasurements of melt index (MI) showing various plastic grades(polyethylene in this case).

FIGS. 6A and 6B are flowcharts of steps, more detailed than those shownin FIG. 5A, performed in accordance with the invention to determineproperties of an unknown polymer sample.

FIG. 7 is a flowchart of the steps to establish an effective industrialmeasurement.

DESCRIPTION

FIG. 1A shows transverse and cross sections, with block diagram inserts,of an NMR apparatus and method where the present invention may be usedto advantage. An industrial process line IPL has material flowing asindicated by arrow A. Some of the material is captured by a probe P andfed through an inlet line LI to a sample region S1. The region isdefined by a tube 98 typically about 30 cm long made of an essentiallynon-magnetic, nonconducting material which does not itself generatesubstantially interfering free induction decay (FID) signals. The tubematerial can be, for example, glass, certain ceramics, certain plastics,or hybrids thereof. The sample region is defined between inlet andoutlet valves V1 and V2. Gas jets J are also provided. These jets arepulsed on/off repeatedly to agitate fluent sample materials duringsample admission and expulsion. A region S2 is the critical portion ofthe sample. It is surrounded by a sample coil 100 tuned to resonance anddriven by a tuning circuit 102 and related transmitter/receivercontroller 104. Grounded loops 101 are Lenz Law shields which areprovided above and below coil 100 to help shape the field of coil 100,i.e., to contain the field established by an excitation pulse. Thecontroller 104 includes an on-board microprocessor and required powersupply elements, memory, program and I/O (input/output) decodingsuitable to interconnect to the hardware shown and to an externalmicrocomputer 106 with associated memory, a keyboard 108, a monitor (orother display) 110, a recorder 112, and/or a process controller 114 (tocontrol the process at IPL). The operator initiates and controlsoperation from the keyboard 108 and the resulting data and signals aresubsequently shown on the display 110 and utilized in the recorder 112and/or the controller 114. The computer 106 also controls instrumentoperation conditions.

The region S2 of tube 98 and coil 100 are in a static, but adjustable,crossing magnetic field defined by a magnetic assembly 116 whichcomprises a yoke 118, pole pieces 120, surrounding Helmholtz coils 124,and a coil current generator 117. The critical sample region S2 of thetube 98 and magnet are contained in a metallic (but non-ferromagnetic)box 126 with highly thermally conductive face-plates 128 and internalpartitions 130 and overall mass related to each other to minimizeharmonics and other interferences with a signal emitted from coil 100 toa sample and/or returned from the sample for pick-up by coil 100 and itstuned circuit 102 and transmit/receive controller 104.

The magnetic assembly, including yoke 118, and other parts therein asshown on FIGS. 1A and 1C, is in turn contained in an environmentalcontrol chamber 132 with optional inert gas fill and purge controls (notshown), an internal electrical heater 134, a motor M driving fan 136,and a temperature sensor 138 in the air stream whose temperature isreflective of the temperature at pole pieces 120 and in the regionsurrounding the air curtain containment jacket 137. Additionally, thereis a heated chamber shown in FIG. 1B where the sample is heated prior tointroduction into the NMR sample chamber. A thermal controller 140processes temperature signals from the sensor 138 to adjustheating/circulation at the heater/fan 134/136 as a coarse control and toadjust current through the Helmholtz coils 124 at magnet pole pieces 120as a sensitive and fast fine control, as well as implementing generalcontrol instructions of computer 106. Further thermal stabilization isprovided by a temperature controlled air curtain consisting oftemperature controlled air 135 circulating inside an air containmentjacket 137, completely surrounding the sample region under NMRmeasurement. The temperature of the air curtain is controlled by theheater thermal control 131B and temperature sensor 131A via 131Daccording to control parameters received from CPU 105-2 (I/O). Sampletemperature is measured by temperature sensor 133 from which an externalcomputer system 106 determines the desired air curtain temperature setpoint to be conveyed to heater thermal control 131B.

While the terms "air" and/or "air curtain" are used herein, it should beunderstood that other gas or liquid environments can be utilized.

FIG. 1B is a schematic outline of the heating chamber 160. The sample isfed (as indicated at arrow 162) into the chamber. There is a heatingelement 164 surrounding the chamber with its temperature set about 10 orso degrees centigrade higher than a mobility enhancing temperature knownfor the specific material. The temperature may be looked up in thebelow-mentioned text. The mobility enhancing temperature of a polymermaterial (e.g., a plastic such as polypropylene, polyethylene, andpolystyrene) is defined herein as follows. It is the temperature thatenhances precision and reliability for property measurements such asmeasurements of viscosity, molecular weight, melt index (MI), flow rateratio (FRR), and melt flow (MF). Molecular weight, MI, FRR, and MF areclosely related. Polymers generally have higher mobilities and anenhanced NMR response when at the mobility enhancing temperature. Themobility enhancing temperature is a minimum or threshold temperature inthat there is a temperature range extending above the mobility enhancingtemperature where the benefits occur without the sample handlingproblems associated with molten polymers. The mobility enhancingtemperature is further defined and explained in the next paragraph.

The mobility enhancing temperature is the temperature at or above the"glass transition" temperature (T_(g)) for amorphous polymers (e.g.,polystyrene), and at or above the "crystalline transition" temperature(T.sub.α) but below the "melting temperature" (T_(m)) for bothcrystalline and semi-crystalline polymers. T_(g), T.sub.α, and T_(m) aredefined herein as temperatures where the following physical phasetransitions take place. At the glass transition temperature (T_(g)), thefractional free volume for amorphous polymers increases appreciably andthe polymer chains start undergoing rapid, almost isotropic motion.Amorphous polymer at this temperature has not melted and still retains asolid-like appearance to the lay observer. This state is sometimesreferred to, in the art, as "rubbery". As a result, the NMR parameters,including time constants (T2's), are influenced. This enables bettercorrelation with, for example, viscosity and MI or MF. For bothcrystalline and semi-crystalline polymers, it is necessary to carry outNMR measurements at or above the crystalline transition temperature(T.sub.α) in order to influence the NMR parameters and obtain betterestimations of, for example, viscosity and MI or MF. In the case of asemi-crystalline polymer, the melting and crystalline transitiontemperatures are greater than the amorphous glass transitiontemperature. Adjusting the sample temperature to such temperatures asjust described enhances NMR precision and reliability for at least themeasurements of the above-identified properties and generally for otherphysical property measurements performed via NMR techniques. Thespecific temperatures as they relate to specific polymers are found invarious handbooks and textbooks such as Textbook of Polymer Science byFred W. Billmeyer, Jr. (Wiley-Interscience, a Division of John Wiley andSons, Inc., 2nd ed., 1971). Again, these temperatures generally arecollectively referred to herein as the mobility enhancing temperature.

The polymers of particular interest herein are polypropylene,polyethylene, and polystyrene. In general, two types of polystyreneexist: "crystal" and "extended". Extended polystyrene is also referredto as "impact" or "rubber extended". The property of polystyrene,regardless whether it is crystal or extended, of most importance hereinis MI. Crystal polystyrene typically has oil added to it, and thus thepercentage of oil (oil %), in addition to MI, can be a property ofinterest to be measured when the sample is a crystal polystyrene.Extended polystyrene typically also has oil and/or rubber added, andtherefore MI, oil %, and the percentage of rubber (rubber %) aremeasurements of interest for extended polystyrene samples. Otherpolymers that can be tested accurately with the NMR system according tothe invention include Ethylene Propylene Rubber, Acryonitrile ButadieneStyrene (ABS) plastic, and Ethylene Vinyl Acetate (EVA) copolymer.

Still referring to FIG. 1B, the sample fed (as indicated at arrow 162)into the chamber remains there long enough to ensure that the sample hasachieved the proper temperature. The temperature need only be within afew degrees of an actual set point. To further ensure proper and uniformheating, a heated temperature controlled nitrogen stream of gas 166 orair is introduced via a port 168 in order to fluidize the material.After two (or more) minutes the sample is introduced into the NMRmeasuring chamber S2 by opening the valve V1. The gas in introduced in adirection and with a pressure to create a turbulence in the materialthat induces movement of particles and homogenization of materialtemperature.

Other embodiments for handling (e.g., heating, measuring, anddiscarding) the samples are possible. For example, referring to FIGS.2A-2D, the sample is heated inside the probe region prior to themeasurement. In general, these other embodiments are better suited to anon-real time, off-line, laboratory environment. In the embodimentillustrated in FIG. 2A, the sample is contained in a sample holder or"basket" 300. The holder 300 itself may be positioned on an air-cylinderdriven mechanical sliding mechanism 302 for physically removing thesample after each measurement by lowering the basket 300 and removingthe sample mechanically and by air jets. This arrangement isparticularly suitable for those samples that stick together during theheating time and cannot be removed by simple air pressure and gravity.In another embodiment illustrated in FIG. 2B, a sample holder 304 ispermanently fixed in position in the center of the probe and a valve 306is located at the bottom of the sample holder 304. The sample is removedby lowering the valve, and the sample is forced out by the heating airdriven from the top. The controlled heating air enters the actual probechamber from the top, and it is forced around and through the sample forthe case of pelletized material (FIG. 2C) or around and through acentral vent pipe for the case of powdered material (FIG. 2D). Theactual heating time is predetermined in each case for adequate heatingto the desired temperature. Sample temperature is determined by athermocouple located just above the sample.

In FIG. 1C, all the electronics and computers and other elements of thesystem controller are grouped for simplicity of description into oneblock 106-1. The temperature of the sample chamber is measured by atemperature sensor 133, such as a silicon junction device orthermocouple, embedded in the chamber wall. FIG. 3 shows anotherpreferred embodiment where the sensor is an infrared (heat) sensor witha window through the sample chamber wall whereby heat radiation impingesthe sensor. The sensor signal is fed through an I/O interface to thecomputer portion of the controller 106-1. In another preferredembodiment, the I/O interface may contain an analog-to-digital (A-to-Dor A/D) converter separated from the computer and/or input to thecomputer via some other communication port, e.g., a serial port. Thecomputer can interrogate the sensor and measure the temperature at anytime, typically at the start of the NMR measurement time period andagain at the end. In response to a temperature change during the NMRmeasurement, the system adjusts the temperature through the heater 131E.The temperature of the heated air 135 is measured by the sensor 131A via131D which is placed in the air path 135. The controller 106-1 maintainsthe sample temperature constant through the heated air within the aircurtain. These two means give the entire system the ability and capacityto maintain the sample temperature essentially constant while theenvironment air around the NMR system changes.

As discussed before, to enable better correlation of the NMR data withviscosity and MI or MF (average molecular weight), amorphous polymersare measured at or above the glass transition temperature, andcrystalline and semi-crystalline polymers are measured at or above thecrystalline transition temperature but below the melting temperature(i.e., polymer samples are measured at the mobility enhancingtemperature as defined above).

Referring back to FIG. 1A, the strength, consistency, and constancy ofthe magnetic field between poles 120 in the region S2 of the sample isthus controlled by a uniform base magnetic field modified by a smallcorrection generated by Helmholtz coils 124 in the entire region S2. TheHelmholtz coils 124 are energized by the coil current controller 177 toaccurately trim the final magnitude of the field in which the sample isplaced. This field is the vector addition of the fields due to themagnet poles 120 and the Helmholtz coils 124. The controller 117 setsthe current through the Helmholtz coils 124 using current generators.The coils 124 are wound around the magnet pole pieces such that themagnetic field created by the current in the coils 124 can add to orsubtract from the field created by the magnet pole pieces. The magnitudeof the current through the coils 124 determines the strength of thefield added to or subtracted from the field due to the magnet polepieces (and related yoke structure) alone.

The actual determination of the current through the Helmholtz coils isaccomplished by carrying out the magnetic energy and resonancetechniques hereinafter described in preliminary runs and adjustingHelmholtz current until the maximum sensitive resonance is achieved.Operating the system this way at resonance or on resonance is thepreferred mode of system operation. In another embodiment, the Helmholtzcurrent is set off resonance by a given offset of about 0.1-3 KHz.

The major elements of electrical controls are tuner 102, including coils100 and 101 and variable capacitors 102-1 and 102-2, resistor 102-3 anddiodes 102-4 and constructed for tuning to Q of twenty to sixty toachieve coil 100 resonance, and control 104 including a transmit/receiveswitch 104-1, a transmitter 104-2 and receiver 104-3, a crystaloscillator 104-4, gated pulse generator (PPG) 104-5, and phase shifter104-6. The crystal provides a nominal twenty Megahertz carrier which isphase modulated or demodulated by the MOD, DEMOD elements of transmitter104-2 and receiver 104-3. The receiver includes variable gain amplifierelements 104-31 and 104-32 for operation. The analog signals receivedare fed to a high speed at least 12 bit flash A/D converter 105-1 andinternal (to the instrument) CPU element 105-2, which provides data toan external computer 106 which has a keyboard 108, monitor 110, modem109, recording elements 112, and process controller elements 114, e.g.,for control of valves VI, V2 via valve controls 115 and/or to coilcurrent controls 117, all via digital-to-analog (D-to-A or D/A)converters (not shown).

The analog signal FID curve is conditioned by a Bessel filter which actsas a pre-filter and an anti-aliasing filter as the subsequent sampling(i.e., digitizing) is usually done at 10 MHz. After digitization, thesignal may be time smoothed by a Fast Fourier Transform, Savitsky-Golay,or other filter or filtering procedure. The combination of these filtersproduces a relative improvement in signal-to-noise ratio which generallyenhances the accuracy of the NMR system.

The excitation of coil 100 and excitation-precession of the sample'sproton content and subsequent relaxation/decay produces a received FMsignal that, after demodulation, controlled gain amplification, and A/Dconversion produces the free induction decay (FID) curve.

As indicated earlier, preferred embodiments are found in the NMRinstrument whether operated in resonance or out of resonance, includingthose preferred embodiments found in the above-listed related patentsand patent applications which have been incorporated by referencehereinto. The following preferred embodiment describes a system andmethod for the analysis of polymers, in particular, polypropylene,polyethylene, and polystyrene.

Referring to FIG. 4, the digitized FID curve data for the material undertest (e.g., polyethylene, polypropylene, or polystyrene) are transmittedto the external computer 106 where a computer program finds the bestcoefficients of the component curves to fit each component of thedigitized FID curve. In the disclosed embodiment, there are threecomponents: an Abragam, a Gaussian, and an exponential. Otherembodiments have more or less than three component curves and othercurve types such as modified exponential and/or trigonometric. Thedetermination of the types of curves which make up the FID curve isimportant because, once the curves are known, they can be extended backto a time origin (shown as A0, B0, and E0 at t0) which is close to thecenter of the transmitted burst signal. This is important since thereare saturation effects of the instrument's electronic gear which occurduring and immediately after the excitation burst signal. During thistime, measurements cannot be accurately taken, yet the region ofinterest under the curve, which is a measure of the number of nuclei inthe sample, extends from the immediate end of the excitation burst towhere the curve is too small to be digitized or is in the noise.

Again, the entire FID curve is decomposed into component curves.Coefficients defining the equations of the component curves are derivedby using an iterative process based upon the Marquardt-Levenberg (M-L)approximation technique. The derivation is applied automatically througha structured realization in software. This M-L technique is used todetermine the magnitude of all the parameters, constants, frequencies,etc. which best fit the FID curve. M-L is an iterative technique wherethe entire curve is determined at once. The M-L iteration processperforms the curve fitting by attempting to minimize the Chi-Squarederror function (the sum of the squared differences between the measureddata points and the data points from the derived equation). The resultsof the M-L approximation are accepted if the Chi-Squared error is smallenough and if the number of iterations to reach an acceptableChi-Squared error does not exceed a preset limit, usually about 30. Ifthe error is not small enough, the M-L fitting procedure may bere-applied with a different set of starting assumptions. If thisre-attempt also fails, the sample is discarded and a new sampleobtained. The M-L technique is documented in the following references:Ind. Appl. Math., vol. 11, pp. 431-444 by D. W. Marquardt, 1963; DataReduction and Error Analysis for the Physical Sciences (New York, McGrawHill) Chapter 11 by Philip R. Bevington, 1969; and The State of the Artin Numerical Analysis (London: Academic Press, David A. H. Jacobs,1977), chapter III.2 by J. E. Dennis. As applied to the measurementregime of interest herein, in a preferred embodiment of the presentinvention, the selected parameters taken from the derived curves are they-axis intercept ratios, time constants, frequency terms, and otherparameters described below.

Other known-in-the-art iterative techniques which may be applied insteadof, or with, the Marquardt-Levenberg include: Gauss-Jordan and "steepestdescent" (found in the above J. E. Dennis reference); Newton-Raphson(known in the art); "partial least squares"; or similar techniquesincluding combinations of these techniques.

One of the major difficulties in making use of iterative curve fittingtechniques (such as Marquardt-Levenberg) is their tendency to reachincorrect solutions. Such solutions frequently (but not always) containparameters which would imply a negative quantity of protons or anexponential "decay" which grows with time. These incorrect solutionslead to serious errors in the result found for a physical sample, forexample, the density or flow properties (e.g., MI) in polyethylene orthe extent of tacticity or MF in polypropylene.

Once the equation of the FID curve is known, each component curve can beextrapolated back to the midpoint of the excitation signal to establishthe intercept of each said component curve.

The resulting data utilized in the computer 106 (FIGS. 1A and 1C) is theequation for the FID curve as composed of a number of component curves.Each of these curves (and their intercepts) has been experimentally andtheoretically related to particular nuclei of interest. In particular,when the FID curve equation is determined, the ratios of the y-axisintercepts, the cross product and squares of these ratios and the decaytimes for each of the curve components, and the product temperature formone or more multidimensional models for the property.

Calibration of the system is accomplished by first measuring a number ofknown samples and using the M-L technique to derive the model equationconstants associated with each known sample. Various non-lineartransforms may then be applied to these constants, usually with the goalof linearizing their relationship to the dependent (i.e., predicted)parameter. Useful non-linear functions include exponential, logarithmic,powers, and cross products of the independent (i.e., measured)parameters.

FIG. 5A is a high-level flowchart of steps performed according to theinvention to determine properties of a polymer sample (e.g., a sample ofplastic such as polypropylene, polyethylene, or polystyrene). The NMRsystem of the invention develops one or more regression equations ormodels for a given property (e.g., two models for MI, one model fordensity, etc.) during a calibration procedure using known samples (steps150-156). The invention relates to those polymer properties for whichtwo or more regression models are developed during calibration withknown samples. In a preferred embodiment, at least two property (e.g.,MI) regression models (e.g., MI₁ and MI₂) are developed (step 156). Aregression model for each of one or more discrete (i.e., two-valued)variables also are developed during calibration (step 156), and thesemodels allow a prediction to be made about which of the plurality of MIregression models should be used for any particular unknown sample (step164). If there are two regression models for the particular polymerproperty of interest, only a single discrete variable regression modelis needed, and the two possible values of the variable correspond to thetwo property models. That is, in the case of two property models, thesingle discrete variable model yields a value (one of the two possiblevalues) which corresponds to the property model that will produce themost accurate measurement of that property for an unknown sample.However, if there are N regression models for the property of interestand N is greater than two, N discrete variable regression models areused and each one corresponds to a different one of the N propertymodels. In this N>2 case, the property model that corresponds to thediscrete variable regression model yielding the largest value isselected as the property model that will produce the most accuratemeasurement of the property of interest for the unknown sample. Ineither case, after having selected one of the plurality of propertyregression models by using the discrete variable regression model(s)(step 164), the selected property model is applied to data derived fromthe unknown sample to determine or estimate a value of the property ofinterest (e.g., MI) of the unknown sample (step 166). In accordance withthe invention, this property measurement is acceptably accurate. In oneembodiment, an acceptable MI measurement made by the NMR system has astandard deviation less than 10% and typically about 5-7% or less.

This standard deviation is the standard deviation between the MIestimation obtained by the NMR system according to the invention and the"actual" MI value (e.g., the MI value obtained by a manual laboratoryASTM method). The "actual" MI value can be determined "off-line" in thelaboratory with a laboratory instrument.

The discrete variable mentioned in the paragraph preceding the aboveparagraph is created ahead of time and prior to model making based on,for example, laboratory density and MI data for polyethylene orlaboratory solubles/tacticity and MF for polypropylene. This data can begathered from various sources such as users of NMR systems conforming tothe descriptions in the above-listed related patents and patentapplications. For example, the discrete variable can have a certainvalue (e.g., 1) for polyethylene with a density≦0.9225 grams/milliliterand an MI≦1.25 and another value (e.g., 0) for any other polyethylenegrade. It should be understood that more complex conditions might beapplied such as density and/or MI within a certain range (e.g.,0.91<density<0.9225 and 0.2<MI<1.25). This discrete variable (orvariables) is referred to sometimes herein as INBOX because its valueindicates whether or not the material is inside of the "box" defined bythe above-identified density/MI range. Using such a discrete variable orvariables (i.e., INBOX₁, INBOX₂, . . . ) results in improved polymerproperty measurements (e.g., MI measurements with a standard deviationless than 10% and typically about 5-7% or less) regardless whether anunknown sample is outside or inside of a particular range or region(e.g., the density/MI range identified above). That is, material forwhich the NMR system might previously have produced unacceptableproperty (e.g., MI) estimations (e.g., plastics falling within theabove-identified range) can now be tested with the NMR system accordingto the invention and an acceptable, useful MI estimation is achieved.

It is important to note that the one or more "boxes" corresponding tothe one or more INBOX variables need not be square or rectangular. Ingeneral, each "box" can have any shape, and each identifies a particularsubset of the data. Each "box" is in general a range or region.

Referring to FIG. 5B, it was discovered that some plastic materials(e.g., polyethylene without hydrogen added during polymerization asindicated by circles 200, 202, and 204) fall within a box defined by theabove-given density/MI range, whereas other plastic materials (e.g.,polyethylene with hydrogen added as indicated by circles 206, 208, 210,and 212) fall outside of the box. For the products outside of the box,the NMR system produced acceptable MI estimations (e.g., a standarddeviation of 8.5%) by prior data analysis schemes. For the productsinside of the box, however, the NMR system produced unacceptable MIestimations (e.g., a standard deviation of about 10-12% or higher). Theinvention addresses this problem by modifying the NMR system such thatit now can more accurately estimate MI, and a variety of other polymerproperties, regardless whether the material being tested is within oroutside of the box. The NMR system according to the inventionautomatically produces polymer property (e.g., MI) estimations which areacceptably accurate (e.g., MI measurements less than 10% and typically5-7% or less) for materials falling both within and outside of the box.

In general, the "grouping" or association of certain polymers to aparticular model is made based on or determined by significant chemicalproperties or process conditions for that polymer group.

In one embodiment, if the sample is a polyethylene sample falling withinthe range, a certain MI regression model is used. If the sample fallsoutside of the range, a different MI regression model is used.Regardless of the particular plastic being sampled, the inventionautomatically selects the MI model which results in the most accurate MImeasurement.

Referring to FIG. 5C, it was discovered that some plastic materials(e.g., material indicated by circles 220, 222, and 224) fall within afirst box defined by the above-given density/MI range, whereas otherplastic materials (e.g., material indicated by circles 226 and 228) falloutside of the first box but inside of a second box, and still otherplastic materials (e.g., material indicated by circles 230 and 232) falloutside of both the first and second boxes. A different INBOX variableis associated with each of the three regions. By using these threevariables, it is possible now to estimate more accurately the particularpolymer property of interest regardless of which box or region thematerial being tested falls into.

In one embodiment, if the sample is a polyethylene sample falling withinthe middle range, the MI regression model associated with that rangewill automatically be selected by the corresponding variable modelyielding the largest value of the other two variable models, and that MImodel is then used to achieve the most accurate MI measurement possible.Note that if the other INBOX variables yield values that are too large,no MI model will be selected and thus no MI estimation or predictionwill be made in these cases.

In accordance with the invention, a plurality of regression models for aproperty other than MI (e.g., xylene solubles, density, rubber/oilcontent, FRR, or MF) can be developed during the calibration procedure(steps 150-156 of FIG. 5A). For example, two or more regression modelsfor rubber content (e.g., R₁ and R₂) can be developed. In this case, thediscrete (e.g., two-valued or Boolean) variable regression model allowsa prediction to be made about which of the two rubber content regressionmodels should be used for any particular unknown sample. After therubber content model is selected by using the discrete variable model,the selected rubber content regression model is applied to data derivedfrom the unknown sample to determine the rubber content of the unknownsample.

Referring again to FIG. 5A, the steps performed in a preferredembodiment of the invention generally are as follows. Samples with knowntypes, properties, and quantities of target nuclei (including flow ratessuch as MI, FRR, and MF in plastics such as polypropylene, polyethylene,and polystyrene) are introduced into the NMR system (step 150). The FIDcurve is digitized via a flash converter of at least 12 bits accuracyand the result is stored in memory. The M-L iterative process is thenused to derive the curve coefficients from the stored FIDs to a givenChi-squared error and iteration limit (step 152). The various non-lineartransformations to be used are then determined to arrive at the variousparameters (step 154). The types, properties, and quantities of targetnuclei in the known samples are then related to the parameters by aregression against these transformed parameters to arrive at aregression equation or model for the discrete INBOX variable orvariables and for each physical property of interest including at leasttwo separate models for at least one polymer property such as MI (step156). These models are stored in the NMR system of the invention inmemory.

Unknown samples can now be introduced into the NMR system. The FID curvefor an unknown sample is recorded and digitized (step 158). The curvecoefficients are then derived (step 160), and the parameters arecalculated (step 162). Based only on the parameters of the unknownsample (and not on any external user-entered information), the INBOXregression model(s) is/are used to arrive at a particular value for thediscrete INBOX variable(s) (step 164). This value (or values)corresponds to one of the polymer property (e.g., MI) regression models,and that model is selected (step 164). In the case of more than tworegression models for a particular polymer property of interest, thesame number of discrete INBOX variable regression models are used, andeach INBOX variable model corresponds to a different one of the propertymodels. In the case of just two property models, only a single INBOXvariable model is used, and each value of the two-valued variablecorresponds to a different one of the property models. In either case,the selected regression model for the property of interest is thenapplied to the parameters derived from the unknown sample to determine avalue for that property of the unknown sample (step 166). The propertycan be estimated for other unknown samples by returning to step 158 andintroducing into the NMR system the next unknown sample, as indicated bya dotted line 168.

Further details of the steps described generally above and withreference to FIG. 5A are presented below with reference to FIGS. 6A and6B.

Referring to FIG. 6A, the first step 36 is to measure samples with knowntypes, properties and, quantities of target nuclei, including flow rates(e.g., MI, FRR, and MF) in plastics (e.g., polyethylene, polypropylene,and polystyrene). This data gathering (step 36) may be done on-line oroff-line. The FID curve is digitized via a flash converter of at least12 bits accuracy and stored in memory. The step 38 is to apply the M-Literative process to derive curve coefficients from the FIDs to a givenChi-squared error. In step 40, the second order "x" variables (i.e., theexplanatory variables) are formed. These x variables can include variousparameters such as ratios of Y-axis intercepts, squares and crossproducts of these ratios, decay times, and temperatures. Higher ordercombinations of these parameters also may be calculated. These xvariables can be thought of as vectors in a multidimensional space wherethe space dimension is equal to the number of the explanatory variables.If there is no multicollinearity among the x variable data, the vectorsare orthogonal in this space (i.e., all dot products are zero). Asmulticollinearity increases, the vectors move away from orthogonality.In the extreme case, there may be perfect correlation between two ormore of the x variables and the vectors will lie on top of one another.An attempted regression analysis of such data would generate singularmatrices. In step 42, x variable data with correlations above a certainthreshold (e.g., 0.99) can be eliminated.

The next step (step 44) is to choose a set of potential explanatoryvariables, the x data, from the M-L derived time equations includingsecond and higher orders of these variables. They are chosen by astepwise technique or some other known technique. In a preferredembodiment, three different sets of x variables are selected and takenthrough the entire remaining steps (steps 46, 48, and 50) and the setgiving the best results is used. In a preferred embodiment, the bestresult is that which results in the lowest adjusted standard deviationof error on the degrees of freedom. One of the three different sets iscomposed of all the x variables. The second set is formed by the knownstepwise technique of adding each new variable and determining if thatvariable helped and then continue adding those variables that help. Thetechnique is also applied in a backwise fashion where each previouslyadded variable is retested in the presence of each new variable. Thethird set is formed by taking all independent variables and variableswith correlations between selected low and high limits, usually 0.2 to0.96.

The independent variables (the "y" variables) represent the property orcharacteristic of interest for the sample being tested. The y variablesare related by a set of linear equations to the x or explanatoryvariables.

The next step (step 46) is to perform a Principal Component Analysis(PCA). Every linear regression model can be restated in terms of a setof orthogonal explanatory (x) variables, where the new variables arelinear combinations of the original x variables. The new x variables arecalled principal components and are orthogonal, thus eliminating theproblem of multicollinearity. The regression model equation using theoriginal explanatory variables is

    Y=Xβ+u                                                (Eq. 1)

where Y is an n-by-1 column matrix of n observations, X is an n-by-pmatrix of n observations on p explanatory variables, β is a p-by-1column matrix of regression coefficients, and u is an n-by-1 columnmatrix of residuals. If it is assumed that the expectation of u is 0,and that the expectation of uu' (wherein u' is the conjugate of u)equals the variance times the identity matrix, and that X and Y havebeen centered and scaled so that the XX' and YY' are matrices ofcorrelation coefficients, then there exists a matrix C, satisfying

    C'(XX')C=Λ                                          (Eq. 2)

and

    C'C=CC'=I                                                  (Eq. 3)

where Λ is a diagonal matrix with ordered Eigenvalues of XX' on thediagonal. The columns of C are the normalized Eigenvectors.

A new set of explanatory variables Z may be formed by Z=XC. These aresummarized as

    Y=Xβ+u=XCC'β+u=Za+u                              (Eq. 4)

where the Z vectors are orthogonal.

This process (step 48) of transforming the x data into z data produces adiagonal matrix Λ of Eigenvalues of the principal components. AnEigenvector of a square matrix A of order n is a nonzero vector v whereAv=λv, and the scalar λ is called an Eigenvalue. Eigenvalues may becalculated for matrix A from

    |A-λI|=0                          (Eq. 5)

where I is the identity matrix, and the corresponding Eigenvectors v maythen be found by solving (A-λI)v=0. The Eigenvalues are sortednumerically from the largest (top left of the diagonal) to the smallest(bottom right). If strong multicollinearity exists, as it does for manyof our explanatory variables, one or more of the lower right diagonalterms will be very small compared with the others and these terms mayapproach zero. If the Eigenvalue is sufficiently close to zero, thevalue of the corresponding z transform of the x data is also essentiallyzero, as given by

    Z.sub.k =F.sub.k (x1,x2, . . . , xn)=0                     (Eq. 6)

where F_(k) is the linear transform derived from PCA.

The relationships of Eq. 6 are used to test each M-L curve fit todetermine whether the x values obtained from M-L comport with those inthe calibration set (derived from the known samples). For this test, astandard set of parameters are used. The set includes five variables:R_(ag) (a ratio of Abragam amplitude/Gaussian amplitude); R_(eg) (aratio of exponential amplitude/Gaussian amplitude); t_(e) (exponentialT2); t_(a) (Abragam T2); and t_(g) (Gaussian T2), and their fifteencross products for a total of twenty variables. The highest existingZ_(k) (usually Z20) is chosen for the test. A seven sigma limit on thevalue of the selected Z_(k) is used, and M-L solutions which result inZ_(k) 's which are outside this range are rejected as M-L fittingfailures.

The orthogonal explanatory (Z) variables are used in the regression todetermine a model equation 48. Since the z variables are orthogonal, thestepwise technique (or another known technique) is a reliable method forselecting terms for use in the regression model.

The steps shown in FIG. 6A and described herein can be used to form aregression model for each property of interest. For example, aregression model for density, a regression model for xylene solubles, aregression model for MI, a regression model for FRR, and a regressionmodel for MF can be formed via the steps detailed in FIG. 6A.

In accordance with the invention, in order to improve the accuracy ofthe estimations of a particular property of interest (e.g., MI), two ormore regression models for the same property are formed. In a preferredembodiment, a plurality of MI regression models are formed, wherein eachMI model is different. Also, according to the invention, a regressionmodel for each discrete variable INBOX is formed. Each INBOX variablecorresponds to a different one of the MI regression models. In apreferred embodiment, the accuracy of MI estimations is improved byforming and utilizing two different MI regression models, and INBOX is atwo-valued or Boolean variable where one value corresponds to one of theMI regression models and the other value of INBOX corresponds to theother MI model.

The regression models formed by the steps of FIG. 6A are applied tounknown samples in the manner indicated by the flowchart of FIG. 6B.Referring now to FIG. 6B, the FID of the unknown sample is measured(step 60), and an M-L analysis is performed (step 62) from which theEigenvectors (i.e., Z_(k) 's) are calculated (step 64). The selectedZ_(k) 's (i.e., those with sufficiently small Eigenvalues) are testedagainst a deviation limitation (step 66) where the determinant of thevector divided by the standard deviation of that vector in thecalibration data will cause a rejection of the M-L solution when theresult is greater than a particular value. In a preferred embodiment,that value is 7, and in other preferred embodiments, the value is 5 or9.

In accordance with the invention, the Z_(k) 's for those M-L solutionswhich pass the limit test (70) are regressed via the model regressionequation to predict the value of the discrete variables INBOX (step 72).As described previously, this value corresponds to one of a plurality ofregression models for a particular property such as MI. In a preferredembodiment, INBOX is a Boolean variable, and two MI regression modelsare provided. One value (e.g., 0) of INBOX corresponds to one of the MImodels, and the other value (e.g., 1) corresponds to the other MI model.As indicated at step 74, the estimated or predicted value of INBOX isused to select one of the plurality of regression models formed for oneof the properties of interest (e.g., to select one of two MI models).With the appropriate MI regression model now identified for this unknownsample, that model is then used to predict an accurate value for MI(step 76). Step 76 involves performing the procedures indicated by steps62, 64, and 66 in order to predict MI.

Note that if the limit test is failed (68), M-L (step 62) is restartedwith different starting assumptions and steps 64 and 66 are repeated.Also, should repeated failures occur with a given unknown sample, thatsample is discarded, and a new unknown sample is taken and the steps ofFIG. 6B are repeated.

Referring to FIGS. 1A, 1C, and 7, to establish effective industrialmeasurements with the NMR system according to the invention, thefollowing procedures are followed. A single FID curve is established tosee if the sample area is clear (Quick FID) in an abbreviated cycle ofattempting to establish an FID curve. If the sample region is not clear(N), measurement is interrupted to allow valve V2 to open and jets J andgravity to clear the region. A new Quick FID step establishes clearance.Then, a new sample is admitted by closing valve V2, opening valve V1,and making such adjustments of probe P and line L1 as may be necessaryto assure sample acquisition. Jets J adjust and stabilize the newsample.

An electronic signal processing apparatus baseline is established in 3to 4 cycles, each having + and - sub-cycles with the addition of C+ andC- to detect a baseline offset and compensate for it.

Further adjustment is established by coils 124 to adjust H0 (i.e.,resonance), and this is enabled by ten to twenty field check cycles ofFID curve generation. The C- FID is subtracted from the C+ FID (thisprocess eliminates small baseline offsets) to obtain a workabledigitized FID signal which has a maximum value at resonance. H0 isadjusted via coil current generator 117 and coils 124 until such maximumis achieved. In another embodiment, H0 may then be changed to offset thesystem by a given amount of about 0.1 to 3 KHz.

Then one or more (usually five to one hundred) measurement cycles areconducted to obtain a useable measurement. Each of these five to onehundred cycles involves a modulated transmission/reception/flash A-Dconversion and storage of data. The curves are then averaged for M-Lcurve fitting, and the above-listed intercepts and ratios areestablished. Similar cycles, often somewhat abbreviated, can be appliedfor Quick FID, field check, and baseline correction purposes. Each ofthe sub-cycles (i.e., + and -) of each such cycle involves a capture andutilization of thousands of FID points in data reduction. Each sub-cycleoccurs on the order of a second, and the number of such sub-cyclesemployed depends on the desired smoothing and signal-to-noise ratio(S/N). Generally, S/N improves in a square root relationship to thenumber of cycles.

As noted in the above-listed related patents and applications, sampletube composition can distort readings. If glass is not used (and it ispreferred to avoid glass in industrial usage), the replacement shouldnot be a hydrocarbon plastic. However, fluorocarbons can be effective inseveral applications since signals from fluorine appear far fromresonance. These signals can be distinguished from hydrogen at thelevels of sensitivity required and if desired can be filtered ordistinguished. In other cases of higher sensitivity measurements (e.g.,for gauging relative proportions of amorphous and crystalline species inmixtures thereof), the sample container should be glass or non-protonicceramic. In some instances, however, fluorocarbon or reinforcedfluorocarbon can be used acceptably for polymer measurements. In allsuch cases, the point is to avoid sample containers with species thatcan couple with transmitted energy and generate a FID decay curvemimicking the samples.

While particular processing and modeling techniques are describedherein, it should be realized by those of ordinary skill in the art thatother techniques can be used instead of or in addition to what isdescribed. For example, neural networks and neural network techniquescan be used with the NMR system to produce estimations of a variety ofproperties for a variety of unknown polymer samples.

Variations, modifications, and other implementations of what isdescribed herein will occur to those of ordinary skill in the artwithout departing from the spirit and the scope of the invention asclaimed. Accordingly, the invention is to be defined not by thepreceding illustrative description but instead by the following claims.

What is claimed is:
 1. A method for measuring polymer properties ofsamples in real time, comprising:storing a plurality of models whereinat least two of the models are useful in predicting a value of aparticular property of a sample and at least one of the models is usefulin identifying which of the at least two models more accurately predictsthe value of the particular property; taking a series of samples of anunknown polymer material from an industrial on-line process and placingeach of the samples successively in a test region; applying a basemagnetic field to the sample in the test region to effect precession ofnuclei of the sample; modifying the precession; receiving a resultingrelaxation signal representative of a free induction decay of the nucleiof the sample; digitizing the free induction decay and analyzing it intocomponent equations; processing the component equations with aniterative technique to derive coefficients representative of thecomponent equations; utilizing the coefficients, or information based onthe coefficients, and the at least one model in order to select one ofthe at least two models; utilizing the coefficients, or informationbased on the coefficients, and the selected one of the at least twomodels in order to predict the value of the particular property; anddiscarding the sample from the test region.
 2. The method of claim 1wherein the step of storing the plurality of models comprises storingthe at least one model which is useful in predicting a value of adiscrete variable having N possible values wherein each of the Npossible values corresponds to a different one of the at least twomodels.
 3. The method of claim 2 wherein the step of storing theplurality of models comprises storing the at least two models which areuseful in predicting the value of melt index (MI) for the sample.
 4. Themethod of claim 3 wherein the step of storing the plurality of modelscomprises storing two models which are useful in predicting the value ofmelt index (MI) for the sample and storing one model which is useful inpredicting the value of a Boolean variable having two possible valueswherein each of the two possible values corresponds to a different oneof the two models.
 5. The method of claim 1 wherein the step of takingthe series of samples of the unknown polymer material from theindustrial on-line process and placing each of the samples successivelyin the test region comprises taking the series of samples which aresamples of polyethylene, polypropylene, or polystyrene.
 6. The method ofclaim 1 wherein the step of storing the plurality of models comprisesstoring more than two first models which are useful in predicting thevalue of the particular property of the sample and storing an equalnumber of second models each of which corresponds to a different one ofthe first models and which are useful in identifying which one of thefirst models more accurately predicts the value of the particularproperty.
 7. Apparatus for measuring polymer properties of samples inreal time, comprising:means for storing a plurality of models wherein atleast two of the models are useful in predicting a value of a particularproperty of a sample and at least one of the models is useful inidentifying which of the at least two models more accurately predictsthe value of the particular property; means for taking a series ofsamples of an unknown polymer material from an industrial on-lineprocess and placing each of the samples successively in a test region;means for applying a base magnetic field to the sample in the testregion to effect precession of nuclei of the sample; means for modifyingthe precession; means for receiving a resulting relaxation signalrepresentative of a free induction decay of the nuclei of the sample;means for digitizing the free induction decay and analyzing it intocomponent equations; means for processing the component equations withan iterative technique to derive coefficients representative of thecomponent equations; means for utilizing the coefficients, orinformation based on the coefficients, and the at least one model inorder to select one of the at least two models; means for utilizing thecoefficients, or information based on the coefficients, and the selectedone of the at least two models in order to predict the value of theparticular property; and means for discarding the sample from the testregion.
 8. The apparatus of claim 7 wherein the means for storing theplurality of models comprises means for storing the at least one modelwhich is useful in predicting a value of a discrete variable having Npossible values wherein each of the N possible values corresponds to adifferent one of the at least two models.
 9. The apparatus of claim 8wherein the means for storing the plurality of models comprises meansfor storing the at least two models which are useful in predicting thevalue of melt index (MI) for the sample.
 10. The apparatus of claim 9wherein the means for storing the plurality of models comprises meansfor storing two models which are useful in predicting the value of meltindex (MI) for the sample and means for storing one model which isuseful in predicting the value of a Boolean variable having two possiblevalues wherein each of the two possible values corresponds to adifferent one of the two models.
 11. The apparatus of claim 7 whereinthe means for taking the series of samples of the unknown polymermaterial from the industrial on-line process and placing each of thesamples successively in the test region comprises means for taking theseries of samples which are samples of polyethylene, polypropylene, orpolystyrene.
 12. The apparatus of claim 7 wherein the means for storingthe plurality of models comprises means for storing more than two firstmodels which are useful in predicting the value of the particularproperty of the sample and storing an equal number of second models eachof which corresponds to a different one of the first models and whichare useful in identifying which one of the first models more accuratelypredicts the value of the particular property.